Overview

Dataset statistics

Number of variables19
Number of observations2550
Missing cells1176
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory378.6 KiB
Average record size in memory152.1 B

Variable types

Numeric11
Categorical8

Alerts

LABEL2013 has constant value "Urban" Constant
LABEL2014 has constant value "Urban" Constant
LABEL2015 has constant value "Urban" Constant
LABEL2016 has constant value "Urban" Constant
LABEL2017 has constant value "Urban" Constant
LABEL2018 has constant value "Urban" Constant
LABEL2019 has constant value "Urban" Constant
LABEL2020 has constant value "Urban" Constant
df_index is highly correlated with LAT and 1 other fieldsHigh correlation
LON is highly correlated with df_index and 2 other fieldsHigh correlation
2013 is highly correlated with 2014 and 2 other fieldsHigh correlation
2014 is highly correlated with LON and 7 other fieldsHigh correlation
2015 is highly correlated with 2013 and 6 other fieldsHigh correlation
2016 is highly correlated with 2013 and 6 other fieldsHigh correlation
2017 is highly correlated with 2014 and 5 other fieldsHigh correlation
2018 is highly correlated with 2014 and 5 other fieldsHigh correlation
2019 is highly correlated with 2014 and 5 other fieldsHigh correlation
2020 is highly correlated with 2014 and 5 other fieldsHigh correlation
LABEL2020 is highly correlated with LABEL2017 and 6 other fieldsHigh correlation
LABEL2017 is highly correlated with LABEL2020 and 6 other fieldsHigh correlation
LABEL2016 is highly correlated with LABEL2020 and 6 other fieldsHigh correlation
LABEL2018 is highly correlated with LABEL2020 and 6 other fieldsHigh correlation
LABEL2019 is highly correlated with LABEL2020 and 6 other fieldsHigh correlation
LABEL2014 is highly correlated with LABEL2020 and 6 other fieldsHigh correlation
LABEL2013 is highly correlated with LABEL2020 and 6 other fieldsHigh correlation
LABEL2015 is highly correlated with LABEL2020 and 6 other fieldsHigh correlation
LAT is highly correlated with df_index and 1 other fieldsHigh correlation
LABEL2013 has 230 (9.0%) missing values Missing
LABEL2014 has 200 (7.8%) missing values Missing
LABEL2015 has 194 (7.6%) missing values Missing
LABEL2016 has 190 (7.5%) missing values Missing
LABEL2017 has 179 (7.0%) missing values Missing
LABEL2018 has 118 (4.6%) missing values Missing
LABEL2019 has 65 (2.5%) missing values Missing
df_index has unique values Unique
2013 has 786 (30.8%) zeros Zeros
2014 has 543 (21.3%) zeros Zeros
2015 has 168 (6.6%) zeros Zeros
2016 has 83 (3.3%) zeros Zeros
2017 has 62 (2.4%) zeros Zeros
2018 has 61 (2.4%) zeros Zeros
2019 has 57 (2.2%) zeros Zeros
2020 has 51 (2.0%) zeros Zeros

Reproduction

Analysis started2022-09-22 15:22:49.803583
Analysis finished2022-09-22 15:23:07.039395
Duration17.24 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2550
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12901.11804
Minimum107
Maximum25800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:07.107204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile6984.45
Q110530.25
median12940.5
Q315419.75
95-th percentile18046.1
Maximum25800
Range25693
Interquartile range (IQR)4889.5

Descriptive statistics

Standard deviation3937.916179
Coefficient of variation (CV)0.3052383651
Kurtosis1.662250451
Mean12901.11804
Median Absolute Deviation (MAD)2462
Skewness-0.1172750626
Sum32897851
Variance15507183.83
MonotonicityStrictly increasing
2022-09-22T20:53:07.252661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1071
 
< 0.1%
145501
 
< 0.1%
145431
 
< 0.1%
145441
 
< 0.1%
145451
 
< 0.1%
145461
 
< 0.1%
145471
 
< 0.1%
145481
 
< 0.1%
145491
 
< 0.1%
145511
 
< 0.1%
Other values (2540)2540
99.6%
ValueCountFrequency (%)
1071
< 0.1%
1081
< 0.1%
1431
< 0.1%
2461
< 0.1%
2471
< 0.1%
3471
< 0.1%
3481
< 0.1%
4251
< 0.1%
4261
< 0.1%
4271
< 0.1%
ValueCountFrequency (%)
258001
< 0.1%
257741
< 0.1%
254731
< 0.1%
254551
< 0.1%
254531
< 0.1%
253991
< 0.1%
253821
< 0.1%
253811
< 0.1%
253801
< 0.1%
253791
< 0.1%

LAT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.43357255
Minimum17.0275
Maximum17.7475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:07.396123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum17.0275
5-th percentile17.3075
Q117.3775
median17.4425
Q317.4975
95-th percentile17.5425
Maximum17.7475
Range0.72
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.08394416492
Coefficient of variation (CV)0.004815086792
Kurtosis1.99202693
Mean17.43357255
Median Absolute Deviation (MAD)0.06
Skewness-0.6344688667
Sum44455.61
Variance0.007046622824
MonotonicityNot monotonic
2022-09-22T20:53:07.523930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.507565
 
2.5%
17.497562
 
2.4%
17.512562
 
2.4%
17.502561
 
2.4%
17.452561
 
2.4%
17.492561
 
2.4%
17.482560
 
2.4%
17.447560
 
2.4%
17.457559
 
2.3%
17.462559
 
2.3%
Other values (90)1940
76.1%
ValueCountFrequency (%)
17.02752
0.1%
17.06252
0.1%
17.06754
0.2%
17.07254
0.2%
17.07754
0.2%
17.08251
 
< 0.1%
17.08751
 
< 0.1%
17.09752
0.1%
17.12752
0.1%
17.15251
 
< 0.1%
ValueCountFrequency (%)
17.74751
 
< 0.1%
17.74252
 
0.1%
17.68751
 
< 0.1%
17.64253
0.1%
17.63755
0.2%
17.63256
0.2%
17.62754
0.2%
17.62255
0.2%
17.61756
0.2%
17.61255
0.2%

LON
Real number (ℝ≥0)

HIGH CORRELATION

Distinct139
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.465233
Minimum78.0475
Maximum78.92751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:07.656314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum78.0475
5-th percentile78.28751
Q178.3975
median78.4675
Q378.53751
95-th percentile78.6125
Maximum78.92751
Range0.88001
Interquartile range (IQR)0.14001

Descriptive statistics

Standard deviation0.1193077387
Coefficient of variation (CV)0.001520517229
Kurtosis2.605373412
Mean78.465233
Median Absolute Deviation (MAD)0.07001
Skewness-0.008685703397
Sum200086.3441
Variance0.01423433651
MonotonicityIncreasing
2022-09-22T20:53:07.776637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.4275153
 
2.1%
78.432553
 
2.1%
78.487552
 
2.0%
78.4825150
 
2.0%
78.462550
 
2.0%
78.422549
 
1.9%
78.477549
 
1.9%
78.417548
 
1.9%
78.437548
 
1.9%
78.517548
 
1.9%
Other values (129)2050
80.4%
ValueCountFrequency (%)
78.04752
 
0.1%
78.052511
 
< 0.1%
78.06252
 
0.1%
78.06752
 
0.1%
78.07256
0.2%
78.077511
0.4%
78.08256
0.2%
78.08755
0.2%
78.11251
 
< 0.1%
78.11752
 
0.1%
ValueCountFrequency (%)
78.927511
 
< 0.1%
78.92251
 
< 0.1%
78.90251
 
< 0.1%
78.89753
 
0.1%
78.89258
0.3%
78.88757
0.3%
78.88257
0.3%
78.87755
0.2%
78.872513
 
0.1%
78.83751
 
< 0.1%

2013
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1765
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1250.724445
Minimum0
Maximum9756.0752
Zeros786
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:07.905934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median585.4917
Q31830.810575
95-th percentile4681.12954
Maximum9756.0752
Range9756.0752
Interquartile range (IQR)1830.810575

Descriptive statistics

Standard deviation1652.865109
Coefficient of variation (CV)1.321526188
Kurtosis3.410446079
Mean1250.724445
Median Absolute Deviation (MAD)585.4917
Skewness1.812238957
Sum3189347.336
Variance2731963.069
MonotonicityNot monotonic
2022-09-22T20:53:08.039379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0786
30.8%
1757.709721
 
< 0.1%
1001.343811
 
< 0.1%
55.668131
 
< 0.1%
16.644651
 
< 0.1%
815.240661
 
< 0.1%
268.209051
 
< 0.1%
313.371121
 
< 0.1%
654.188481
 
< 0.1%
1121.215581
 
< 0.1%
Other values (1755)1755
68.8%
ValueCountFrequency (%)
0786
30.8%
0.518941
 
< 0.1%
1.985351
 
< 0.1%
4.022181
 
< 0.1%
4.643641
 
< 0.1%
4.783651
 
< 0.1%
6.970211
 
< 0.1%
7.08651
 
< 0.1%
9.240151
 
< 0.1%
13.751331
 
< 0.1%
ValueCountFrequency (%)
9756.07521
< 0.1%
9685.373051
< 0.1%
9536.605471
< 0.1%
9107.619141
< 0.1%
9064.57521
< 0.1%
8957.394531
< 0.1%
8872.73731
< 0.1%
8367.245121
< 0.1%
8280.754881
< 0.1%
8267.025391
< 0.1%

2014
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2008
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2296.014009
Minimum0
Maximum11829.86328
Zeros543
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:08.172887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1199.4760925
median1287.46558
Q33809.03705
95-th percentile7580.554174
Maximum11829.86328
Range11829.86328
Interquartile range (IQR)3609.560957

Descriptive statistics

Standard deviation2503.449557
Coefficient of variation (CV)1.090345942
Kurtosis0.2357906393
Mean2296.014009
Median Absolute Deviation (MAD)1287.46558
Skewness1.091058302
Sum5854835.723
Variance6267259.684
MonotonicityNot monotonic
2022-09-22T20:53:08.300308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0543
 
21.3%
4389.752931
 
< 0.1%
5059.282231
 
< 0.1%
5950.633791
 
< 0.1%
6278.625981
 
< 0.1%
7814.082521
 
< 0.1%
8565.861331
 
< 0.1%
7154.264651
 
< 0.1%
6590.292971
 
< 0.1%
7796.449711
 
< 0.1%
Other values (1998)1998
78.4%
ValueCountFrequency (%)
0543
21.3%
1.985351
 
< 0.1%
4.022181
 
< 0.1%
4.643641
 
< 0.1%
4.783651
 
< 0.1%
7.08651
 
< 0.1%
9.240151
 
< 0.1%
12.310761
 
< 0.1%
13.724631
 
< 0.1%
15.864991
 
< 0.1%
ValueCountFrequency (%)
11829.863281
< 0.1%
10705.528321
< 0.1%
10586.008791
< 0.1%
10238.82911
< 0.1%
10185.724611
< 0.1%
10183.809571
< 0.1%
10181.17481
< 0.1%
10115.186521
< 0.1%
9963.309571
< 0.1%
9932.448241
< 0.1%

2015
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2383
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3779.467698
Minimum0
Maximum13333.70801
Zeros168
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:08.426724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11531.422518
median3523.53894
Q35814.852418
95-th percentile8311.085203
Maximum13333.70801
Range13333.70801
Interquartile range (IQR)4283.4299

Descriptive statistics

Standard deviation2658.114725
Coefficient of variation (CV)0.7033039935
Kurtosis-0.693980699
Mean3779.467698
Median Absolute Deviation (MAD)2132.384885
Skewness0.3863947061
Sum9637642.63
Variance7065573.893
MonotonicityNot monotonic
2022-09-22T20:53:08.551884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0168
 
6.6%
8113.099121
 
< 0.1%
7154.897461
 
< 0.1%
5587.112791
 
< 0.1%
6762.306151
 
< 0.1%
7116.300781
 
< 0.1%
3526.509031
 
< 0.1%
4718.714841
 
< 0.1%
9823.081051
 
< 0.1%
9275.000981
 
< 0.1%
Other values (2373)2373
93.1%
ValueCountFrequency (%)
0168
6.6%
13.724631
 
< 0.1%
18.68061
 
< 0.1%
21.566861
 
< 0.1%
22.544731
 
< 0.1%
24.26031
 
< 0.1%
28.721121
 
< 0.1%
34.096491
 
< 0.1%
41.014321
 
< 0.1%
49.115551
 
< 0.1%
ValueCountFrequency (%)
13333.708011
< 0.1%
13245.873051
< 0.1%
12632.67091
< 0.1%
11832.882811
< 0.1%
11765.667971
< 0.1%
11589.76661
< 0.1%
11110.63771
< 0.1%
10918.51661
< 0.1%
10761.903321
< 0.1%
10725.008791
< 0.1%

2016
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2468
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5187.219477
Minimum0
Maximum15308.28418
Zeros83
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:08.678412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile533.0027955
Q12976.717893
median5239.228515
Q37354.420412
95-th percentile9596.914061
Maximum15308.28418
Range15308.28418
Interquartile range (IQR)4377.70252

Descriptive statistics

Standard deviation2851.520145
Coefficient of variation (CV)0.5497203575
Kurtosis-0.6527784932
Mean5187.219477
Median Absolute Deviation (MAD)2169.234745
Skewness0.06510166397
Sum13227409.67
Variance8131167.139
MonotonicityNot monotonic
2022-09-22T20:53:08.973533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
083
 
3.3%
3130.107911
 
< 0.1%
9824.619141
 
< 0.1%
8562.401371
 
< 0.1%
8828.8751
 
< 0.1%
3742.336671
 
< 0.1%
8649.899411
 
< 0.1%
7407.218751
 
< 0.1%
4270.502441
 
< 0.1%
3928.368161
 
< 0.1%
Other values (2458)2458
96.4%
ValueCountFrequency (%)
083
3.3%
10.712521
 
< 0.1%
11.735221
 
< 0.1%
21.566861
 
< 0.1%
28.721121
 
< 0.1%
71.119141
 
< 0.1%
77.249561
 
< 0.1%
102.969631
 
< 0.1%
106.126661
 
< 0.1%
128.142751
 
< 0.1%
ValueCountFrequency (%)
15308.284181
< 0.1%
15274.263671
< 0.1%
13830.876951
< 0.1%
13477.228521
< 0.1%
13212.479491
< 0.1%
13006.553711
< 0.1%
12857.413091
< 0.1%
12790.96681
< 0.1%
12771.906251
< 0.1%
12601.276371
< 0.1%

2017
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2489
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5521.008381
Minimum0
Maximum15504.71875
Zeros62
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:09.105699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile686.481136
Q13432.009278
median5656.745115
Q37629.78455
95-th percentile9721.926221
Maximum15504.71875
Range15504.71875
Interquartile range (IQR)4197.775272

Descriptive statistics

Standard deviation2794.124812
Coefficient of variation (CV)0.5060895799
Kurtosis-0.5298231896
Mean5521.008381
Median Absolute Deviation (MAD)2089.33106
Skewness-0.01493390579
Sum14078571.37
Variance7807133.465
MonotonicityNot monotonic
2022-09-22T20:53:09.224882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062
 
2.4%
3651.014651
 
< 0.1%
9477.867191
 
< 0.1%
3711.586911
 
< 0.1%
8947.882811
 
< 0.1%
8125.495611
 
< 0.1%
4810.069821
 
< 0.1%
4334.555661
 
< 0.1%
3416.970461
 
< 0.1%
5018.678221
 
< 0.1%
Other values (2479)2479
97.2%
ValueCountFrequency (%)
062
2.4%
10.7681
 
< 0.1%
21.566861
 
< 0.1%
29.415181
 
< 0.1%
47.287381
 
< 0.1%
57.976141
 
< 0.1%
71.119141
 
< 0.1%
77.212771
 
< 0.1%
91.704981
 
< 0.1%
100.865751
 
< 0.1%
ValueCountFrequency (%)
15504.718751
< 0.1%
15395.755861
< 0.1%
13878.332031
< 0.1%
13849.601561
< 0.1%
13776.42481
< 0.1%
13468.877931
< 0.1%
13252.11231
< 0.1%
13139.763671
< 0.1%
12893.333981
< 0.1%
12794.596681
< 0.1%

2018
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2490
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5564.770814
Minimum0
Maximum15504.71875
Zeros61
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:09.355016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile758.727235
Q13458.108215
median5697.28882
Q37665.206302
95-th percentile9809.960892
Maximum15504.71875
Range15504.71875
Interquartile range (IQR)4207.098088

Descriptive statistics

Standard deviation2794.526635
Coefficient of variation (CV)0.502181802
Kurtosis-0.5183716348
Mean5564.770814
Median Absolute Deviation (MAD)2100.90674
Skewness-0.01661055651
Sum14190165.58
Variance7809379.115
MonotonicityNot monotonic
2022-09-22T20:53:09.476722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
 
2.4%
3416.970461
 
< 0.1%
9228.577151
 
< 0.1%
9477.867191
 
< 0.1%
3795.384771
 
< 0.1%
8991.989261
 
< 0.1%
8125.495611
 
< 0.1%
4810.069821
 
< 0.1%
4334.555661
 
< 0.1%
3651.014651
 
< 0.1%
Other values (2480)2480
97.3%
ValueCountFrequency (%)
061
2.4%
10.7681
 
< 0.1%
21.566861
 
< 0.1%
47.287381
 
< 0.1%
57.976141
 
< 0.1%
71.119141
 
< 0.1%
77.212771
 
< 0.1%
91.704981
 
< 0.1%
100.865751
 
< 0.1%
103.336171
 
< 0.1%
ValueCountFrequency (%)
15504.718751
< 0.1%
15395.755861
< 0.1%
13878.332031
< 0.1%
13849.601561
< 0.1%
13776.42481
< 0.1%
13468.877931
< 0.1%
13252.238281
< 0.1%
13188.445311
< 0.1%
13139.763671
< 0.1%
12893.333981
< 0.1%

2019
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2494
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5670.976333
Minimum0
Maximum15504.71875
Zeros57
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:09.606533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile783.1089885
Q13577.907655
median5851.732665
Q37816.106325
95-th percentile9919.668601
Maximum15504.71875
Range15504.71875
Interquartile range (IQR)4238.19867

Descriptive statistics

Standard deviation2819.175989
Coefficient of variation (CV)0.497123568
Kurtosis-0.4910815944
Mean5670.976333
Median Absolute Deviation (MAD)2070.17749
Skewness-0.03624552865
Sum14460989.65
Variance7947753.256
MonotonicityNot monotonic
2022-09-22T20:53:09.727672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057
 
2.2%
4291.47511
 
< 0.1%
9708.227541
 
< 0.1%
9283.750981
 
< 0.1%
9496.388671
 
< 0.1%
3795.384771
 
< 0.1%
9113.276371
 
< 0.1%
8210.550781
 
< 0.1%
4807.910161
 
< 0.1%
3404.247561
 
< 0.1%
Other values (2484)2484
97.4%
ValueCountFrequency (%)
057
2.2%
10.7681
 
< 0.1%
47.276381
 
< 0.1%
47.287381
 
< 0.1%
57.976141
 
< 0.1%
71.119141
 
< 0.1%
91.704981
 
< 0.1%
100.865751
 
< 0.1%
103.336171
 
< 0.1%
108.84531
 
< 0.1%
ValueCountFrequency (%)
15504.718751
< 0.1%
15398.26271
< 0.1%
14757.65431
< 0.1%
14688.92481
< 0.1%
13878.332031
< 0.1%
13858.428711
< 0.1%
13776.42481
< 0.1%
13468.877931
< 0.1%
13252.238281
< 0.1%
13139.763671
< 0.1%

2020
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2500
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5746.529868
Minimum0
Maximum15504.71875
Zeros51
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size20.0 KiB
2022-09-22T20:53:09.857661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile904.256095
Q13666.710145
median5980.066895
Q37877.377077
95-th percentile9988.177781
Maximum15504.71875
Range15504.71875
Interquartile range (IQR)4210.666932

Descriptive statistics

Standard deviation2815.937566
Coefficient of variation (CV)0.4900240023
Kurtosis-0.4845828368
Mean5746.529868
Median Absolute Deviation (MAD)2043.466305
Skewness-0.05861127092
Sum14653651.16
Variance7929504.373
MonotonicityNot monotonic
2022-09-22T20:53:09.978431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
051
 
2.0%
5076.821291
 
< 0.1%
4060.080321
 
< 0.1%
9161.931641
 
< 0.1%
8210.550781
 
< 0.1%
4807.910161
 
< 0.1%
4291.47511
 
< 0.1%
3404.247561
 
< 0.1%
3653.000241
 
< 0.1%
4796.002931
 
< 0.1%
Other values (2490)2490
97.6%
ValueCountFrequency (%)
051
2.0%
10.7681
 
< 0.1%
47.276381
 
< 0.1%
47.287381
 
< 0.1%
57.976141
 
< 0.1%
70.726381
 
< 0.1%
78.516961
 
< 0.1%
91.704981
 
< 0.1%
100.865751
 
< 0.1%
103.336171
 
< 0.1%
ValueCountFrequency (%)
15504.718751
< 0.1%
15398.26271
< 0.1%
14757.65431
< 0.1%
14688.92481
< 0.1%
13914.39161
< 0.1%
13878.332031
< 0.1%
13858.428711
< 0.1%
13468.877931
< 0.1%
13252.02931
< 0.1%
13139.787111
< 0.1%

LABEL2013
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing230
Missing (%)9.0%
Memory size20.0 KiB
Urban
2320 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11600
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2320
91.0%
(Missing)230
 
9.0%

Length

2022-09-22T20:53:10.094734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:53:10.180394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2320
100.0%

Most occurring characters

ValueCountFrequency (%)
U2320
20.0%
r2320
20.0%
b2320
20.0%
a2320
20.0%
n2320
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9280
80.0%
Uppercase Letter2320
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2320
25.0%
b2320
25.0%
a2320
25.0%
n2320
25.0%
Uppercase Letter
ValueCountFrequency (%)
U2320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2320
20.0%
r2320
20.0%
b2320
20.0%
a2320
20.0%
n2320
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2320
20.0%
r2320
20.0%
b2320
20.0%
a2320
20.0%
n2320
20.0%

LABEL2014
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing200
Missing (%)7.8%
Memory size20.0 KiB
Urban
2350 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11750
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2350
92.2%
(Missing)200
 
7.8%

Length

2022-09-22T20:53:10.251277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:53:10.335365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2350
100.0%

Most occurring characters

ValueCountFrequency (%)
U2350
20.0%
r2350
20.0%
b2350
20.0%
a2350
20.0%
n2350
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9400
80.0%
Uppercase Letter2350
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2350
25.0%
b2350
25.0%
a2350
25.0%
n2350
25.0%
Uppercase Letter
ValueCountFrequency (%)
U2350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11750
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2350
20.0%
r2350
20.0%
b2350
20.0%
a2350
20.0%
n2350
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2350
20.0%
r2350
20.0%
b2350
20.0%
a2350
20.0%
n2350
20.0%

LABEL2015
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing194
Missing (%)7.6%
Memory size20.0 KiB
Urban
2356 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11780
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2356
92.4%
(Missing)194
 
7.6%

Length

2022-09-22T20:53:10.406363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:53:10.490785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2356
100.0%

Most occurring characters

ValueCountFrequency (%)
U2356
20.0%
r2356
20.0%
b2356
20.0%
a2356
20.0%
n2356
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9424
80.0%
Uppercase Letter2356
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2356
25.0%
b2356
25.0%
a2356
25.0%
n2356
25.0%
Uppercase Letter
ValueCountFrequency (%)
U2356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11780
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2356
20.0%
r2356
20.0%
b2356
20.0%
a2356
20.0%
n2356
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2356
20.0%
r2356
20.0%
b2356
20.0%
a2356
20.0%
n2356
20.0%

LABEL2016
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing190
Missing (%)7.5%
Memory size20.0 KiB
Urban
2360 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2360
92.5%
(Missing)190
 
7.5%

Length

2022-09-22T20:53:10.560797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:53:10.645238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2360
100.0%

Most occurring characters

ValueCountFrequency (%)
U2360
20.0%
r2360
20.0%
b2360
20.0%
a2360
20.0%
n2360
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9440
80.0%
Uppercase Letter2360
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2360
25.0%
b2360
25.0%
a2360
25.0%
n2360
25.0%
Uppercase Letter
ValueCountFrequency (%)
U2360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2360
20.0%
r2360
20.0%
b2360
20.0%
a2360
20.0%
n2360
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2360
20.0%
r2360
20.0%
b2360
20.0%
a2360
20.0%
n2360
20.0%

LABEL2017
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing179
Missing (%)7.0%
Memory size20.0 KiB
Urban
2371 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11855
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2371
93.0%
(Missing)179
 
7.0%

Length

2022-09-22T20:53:10.715849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:53:10.799560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2371
100.0%

Most occurring characters

ValueCountFrequency (%)
U2371
20.0%
r2371
20.0%
b2371
20.0%
a2371
20.0%
n2371
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9484
80.0%
Uppercase Letter2371
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2371
25.0%
b2371
25.0%
a2371
25.0%
n2371
25.0%
Uppercase Letter
ValueCountFrequency (%)
U2371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11855
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2371
20.0%
r2371
20.0%
b2371
20.0%
a2371
20.0%
n2371
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2371
20.0%
r2371
20.0%
b2371
20.0%
a2371
20.0%
n2371
20.0%

LABEL2018
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing118
Missing (%)4.6%
Memory size20.0 KiB
Urban
2432 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12160
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2432
95.4%
(Missing)118
 
4.6%

Length

2022-09-22T20:53:10.872416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:53:10.957542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2432
100.0%

Most occurring characters

ValueCountFrequency (%)
U2432
20.0%
r2432
20.0%
b2432
20.0%
a2432
20.0%
n2432
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9728
80.0%
Uppercase Letter2432
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2432
25.0%
b2432
25.0%
a2432
25.0%
n2432
25.0%
Uppercase Letter
ValueCountFrequency (%)
U2432
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12160
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2432
20.0%
r2432
20.0%
b2432
20.0%
a2432
20.0%
n2432
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2432
20.0%
r2432
20.0%
b2432
20.0%
a2432
20.0%
n2432
20.0%

LABEL2019
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing65
Missing (%)2.5%
Memory size20.0 KiB
Urban
2485 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12425
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2485
97.5%
(Missing)65
 
2.5%

Length

2022-09-22T20:53:11.028177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:53:11.113197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2485
100.0%

Most occurring characters

ValueCountFrequency (%)
U2485
20.0%
r2485
20.0%
b2485
20.0%
a2485
20.0%
n2485
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9940
80.0%
Uppercase Letter2485
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2485
25.0%
b2485
25.0%
a2485
25.0%
n2485
25.0%
Uppercase Letter
ValueCountFrequency (%)
U2485
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12425
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2485
20.0%
r2485
20.0%
b2485
20.0%
a2485
20.0%
n2485
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2485
20.0%
r2485
20.0%
b2485
20.0%
a2485
20.0%
n2485
20.0%

LABEL2020
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
Urban
2550 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12750
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2550
100.0%

Length

2022-09-22T20:53:11.185835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:53:11.273590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2550
100.0%

Most occurring characters

ValueCountFrequency (%)
U2550
20.0%
r2550
20.0%
b2550
20.0%
a2550
20.0%
n2550
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10200
80.0%
Uppercase Letter2550
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2550
25.0%
b2550
25.0%
a2550
25.0%
n2550
25.0%
Uppercase Letter
ValueCountFrequency (%)
U2550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12750
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2550
20.0%
r2550
20.0%
b2550
20.0%
a2550
20.0%
n2550
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII12750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2550
20.0%
r2550
20.0%
b2550
20.0%
a2550
20.0%
n2550
20.0%

Interactions

2022-09-22T20:53:04.685761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:50.617063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.842526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:53.150816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:54.551396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.840402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:57.189831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:58.720660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:00.118173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:01.412155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:03.136296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:04.808565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:50.724695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.952678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:53.254341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:54.666285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.949205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:57.311056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:58.846367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:00.230528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:01.704725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:03.255202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:04.938110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:50.837509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:52.070293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:53.365040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:54.791368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:56.065475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:57.445437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:58.982417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:00.349206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:01.843931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:03.438206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:05.058741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:50.937144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:52.173321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:53.465447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:54.902168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:56.170514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:57.554916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:59.103291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:00.457998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:01.959408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:03.565186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:05.177276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.043512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:52.280383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:53.573683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.018321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:56.281509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:57.847075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:59.229908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:00.569876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:02.075698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:03.684809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:05.294293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.154450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:52.391858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:53.852521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.133571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:56.390936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:57.965361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:59.369020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:00.684508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:02.191011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:03.806865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:05.423608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.266026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:52.507221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:53.963011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.254529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:56.516440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:58.087413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:59.496343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:00.802221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:02.329683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:03.953247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:05.550406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.381373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:52.632983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:54.078184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.371897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:56.638558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:58.204034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:59.619303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:00.923249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:02.634823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:04.118764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:05.674451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.497354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:52.763795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:54.189155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.490947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:56.774741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:58.324359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:59.740599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:01.046660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:02.763504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:04.260806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:05.976673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.612399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:52.894416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:54.308081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.609432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:56.917339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:58.446388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:59.869305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:01.167434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:02.892253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:04.422474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:06.099429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:51.729124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:53.020281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:54.427951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:55.725063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:57.059468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:58.584617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:52:59.996541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:01.287994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:03.014682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:53:04.550100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-22T20:53:11.347127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T20:53:11.692148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T20:53:11.853284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T20:53:11.996225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-22T20:53:12.133866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T20:53:06.307905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T20:53:06.623158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-22T20:53:06.798229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-22T20:53:06.938188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexLATLON20132014201520162017201820192020LABEL2013LABEL2014LABEL2015LABEL2016LABEL2017LABEL2018LABEL2019LABEL2020
010717.377578.047500.00.00.000000.000000.000000.000000.000000.00000UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
110817.382578.047500.00.00.000000.00000550.80963550.80963550.80963550.80963UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
214317.377578.052510.00.00.000000.00000355.16251355.16251355.16251355.16251UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
324617.332578.062500.00.02759.319342761.860842761.860842761.860842761.860843594.86499UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
424717.337578.062500.00.0603.91541603.91541603.91541603.91541603.91541994.47986UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
534717.622578.067500.00.0589.46979589.46979589.46979589.46979589.46979589.46979UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
634817.627578.067500.00.0592.54913592.54913592.54913592.54913771.26556771.26556UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
742517.607578.072500.00.02093.801762093.801762093.801762093.801762055.732672055.73267UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
842617.612578.072500.00.06871.573247579.710456865.416996865.416996919.686046918.69287UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
942717.617578.072500.00.03257.407473290.535163290.535163290.535163282.270753282.41064UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban

Last rows

df_indexLATLON20132014201520162017201820192020LABEL2013LABEL2014LABEL2015LABEL2016LABEL2017LABEL2018LABEL2019LABEL2020
25402537917.522578.8925059.8942688.6025882.707286456.617686459.451666459.451666391.639656493.99268UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
25412538017.527578.89250560.89545545.351261945.492923845.485356807.556646807.556646854.757817330.81494UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
25422538117.532578.892500.000000.00000899.63824899.638243701.611333701.611333687.563233687.56323UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
25432538217.537578.892500.000000.000000.000000.000000.000000.000000.000000.00000UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
25442539917.252578.89750575.61755559.93976559.93976559.939761736.652103628.388434064.404304064.40430UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
25452545317.522578.897500.000000.000000.000001660.811651660.811651660.811651634.377932400.31177UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
25462545517.532578.89750138.73227120.56501726.67505726.675051761.581791761.581791787.536991886.89514UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
25472547317.252578.90250565.82843565.96167909.98468909.987792067.621833230.786873531.388183531.38818NoneNoneNoneNoneNoneNoneNoneUrban
25482577417.462578.9225081.453610.000000.000000.000000.000000.000000.000000.00000UrbanUrbanUrbanUrbanUrbanUrbanUrbanUrban
25492580017.232578.927510.000000.000000.000000.000000.000000.000000.000000.00000NoneNoneNoneNoneNoneNoneNoneUrban